Toward optimization of a wind/
compressed air energy storage
(CAES) power system
Jeffery B. Greenblatt
Samir Succar
David C. Denkenberger
Robert H. Williams
Princeton University, Princeton, NJ 08544
Guyot Hall, (609) 258-7442 / 7715 FAX, jgreenbl@princeton.edu
Electric Power Conference, Baltimore, MD, 30 March – 1 April 2004
Session 11D (Wind Power II), 1 April 2004
Foote Creek Rim, Wyoming
Does wind power need storage?
2. Boost wind capacity factor
at large market penetration
(offsets fuel cost only)
Time
Time
Few markets
currently exist
Value
1. Make wind dispatchable
(price arbitrage; potential
at small market share)
Power
Three contexts:
Market share
3. Exploit high-quality but
remote wind resources
(by reducing transmission
costs)
Electric storage options
Cost of 20
Capacity Storage hrs. storage
Technology
($/kW) ($/kWh) ($/kW)
1
Compressed Air Energy
350
370
Storage (CAES) (350 MW)
10
Pumped hydroelectric
900
1100
100
Advanced battery (10 MW) 120
2100
300
Flywheel (100 MW)
150
6200
300
Superconductor (100 MW) 120
6100
Source: Schainker, 1997 (reproduced in PCAST, 1999)
CAES is clear choice for:
• Several hours (or more) of storage
• Large capacity (> ~100 MW)
CAES system
Compressor train
Expander/generator train
Air
Exhaust
PC
PG
Intercoolers
PC = Compressor
power in
PG = Generator
power out
Aquifer,
salt cavern,
or hard mine
Heat recuperator
Fuel (e.g. natural gas, distillate)
Air
Storage
hS = Hours of
Storage (at PC)
A wind/CAES model
PWF
PT
CAES plant
Wind farm
PWF = Wind Farm max.
power out
(rated power)
Transmission
Underground
air storage
PT = Transmission line
max. power
For this application CAES is needed to provide baseload power
Research objectives
• What is optimal wind/CAES system for baseload
power transmission?
• What is optimal capacity factor (CF) of that
transmission line?
• How much will such a system cost, and can it
compete against other baseload systems (nuclear,
coal, natural gas)?
Note: Costs of system components were not
available in time for the Feb. 2 deadline. If
component costs can be obtained, a cost
optimization will be presented at the conference.
Key parameters
• Size of CAES generation relative to
transmission line (PG/PT)
• CAES compression/generation ratio
(PC/PG)
• Relative size of wind farm (PWF/PT)
• CAES storage time relative to wind
autocorrelation time (hS/hA)
• Ratio of turbine speed rating to
resource wind speed (vrate/vavg)
Gen
Comp
hS
vavg.
vrate
Gen
hA
Secondary parameters
• CAES electricity output/input
ratio (Eo/Ei)
• Wind turbine array spacing
(xD2)
• Weibull shape parameter (k)
and wind power density (Pwind)
Ei
Eo
Wind farm simulation
(k2 > k1)
Power curve
PWF
Wind speed
Wind speed
Losses
Wind power time series
Autocorrelation
time (hA)
Time
Wind speed
Wind speed time series
Wind speed
Rated power
Power
Probability
Weibull dist.
} Power “lost” Rated power
Time
CAES model
Spilled power
(if storage full)
PC
CO2
Compressor
Air
Losses
Spilled power
CAES
capacity
Transmission
line
capacity
PWF
Generator
Losses
X
hS
PG
Air
storage
Direct output
(≤ PT)
Fuel
Transmission
losses
Total system
output (≤ PT)
Base case configuration
Wind resource:
System
CF = 0.80
k = 3, vavg = 9.6 m/s,
Pwind = 550 W/m2 (Class 5)
hA = 5 hrs.
Wind farm:
PWF = 2 PT (4000 MW)
Spacing = 50 D2
vrated = 1.4 vavg
PC = 0.85 PT
(1700 MW)
PG = 0.50 PT
(1000 MW)
Comp
Gen
hS = 10 hrs.
(at PC)
Eo/Ei = 1.30
CAES system
Transmission:
PT = 2000 MW
Compressor and generator sizes
1.5
Cut along constant PG/PT:
Base case
PC/PT
1
CF
Base case
CF = 81%
0.5
CF = 76%
PC/PT
CF = 72%
CF = 68%
0
0.5
1
PG/PT
1.5
CF improves (with
diminishing returns)
as either PC/PT or
PG/PT increases
Compressor/generator ratio
1.5
Max. CF
= 85%
Slope ~ 1.7
Base case
PC/PT
1
Minimal increase in CF
for PG/PT = 0.5  1
CF = 81%
0.5
CF = 76%
Slope expected to be
controlled by PWF/PT
and turbine rating
CF = 72%
CF = 68%
0
0.5
1
PG/PT
For given CF, least cost
configuration appears
to lie along slope line
1.5
Wind farm parameters
Base case
CF
Base case
Small change in CF
with array spacing
PWF
= PT
case
PWF/PT (oversizing)
Array spacing (D2)
Some improvement at large PWF/PT, but
most improvement at PWF/PT ≤ 2
Storage vs. autocorrelation time
Cut along constant hS:
Base case
10
CF
Storage time (hS)
(hrs. log scale)
100
1
Base
case hS = hA
case
hA (hrs. log scale)
0.1
0.1
No improvement in
1
10
100 CF if hS >> hA or
Autocorrelation time (hA)
vice-versa
(hrs. log scale)
Power derating
vrate = 1.0vavg
As vrate decreases,
turbines run at rated
(maximum) power more
of the time
Probabilityweighted power
Wind speed
7% above
rated speed
Probabilityweighted power
vrate = 1.4vavg
Probabilityweighted power
Power
Wind turbine power curve
vrate = 1.8vavg
Wind speed
36%
CF increases,
Wind speed but rated power
decreases, so
72%
more turbines
needed for same
PWF
Wind speed
CAES generation vs. turbine rating
0.6
Base case
(“large CAES”)
Large vrate/vavg
CF = 40%
0.5
PG/PT
0.4
0.3
0.2
Alternative case
(“small CAES”):
Small vrate/vavg
0.1
0
1
1.5
vrate/vavg
2
Small CAES case may
be more economical if
(COSTWT•NWT) +
COSTCAES < 0
Alternatively, PWF/PT could be increased (may be more expensive)
Dependence on Eo/Ei
CF
Base case
Little change in CF
with CAES efficiency
Eo/Ei
Wind resource parameters
Base case
1.4
CF
Base case
vrate/vavg
1.0
1.8
Pwind (W/m2)
Virtually no change in CF
over Pwind = 200-1000
W/m2 (classes 2-7+)
Weibull k
CF trend with k
depends strongly on
vrate/vavg
Conclusions
• Capacity factor (CF) of 80% is achievable
for our base case:
PWF/PT = 2
hS = 10 h
PG/PT = 0.5
PC/PG = 1.7
spacing = 50 D2 vrate/vavg = 1.4
• Base case is somewhat improved by
increasing PWF/PT, PG/PT or array spacing,
but all likely to be expensive
• Optimal storage time (hS) should be
somewhat larger than the wind
autocorrelation time (hA)
Base case
CF = 80%
Gen
hS >
hA
Conclusions (cont’d)
• Comparable CF is achieved by reducing
CAES system size and rating turbines lower
CAES +
(alternatively, PWF/PT could be increased but
size
this is probably more expensive).
• Dependence of CF on k is coupled to
turbine rating, with CF increasing with k for
lower vrate/vavg, and decreasing for higher
vrate/vavg.
• Changing Eo/Ei, Pwind has little effect on CF. Ei Eo
Acknowledgments
• Dennis Elliott, Michael Milligan, Marc Schwarz,
and Yih-Wei Wan, NREL
• Al Dutcher, HPRCC
• Marc Kapner, Austin Energy
• Nisha Desai, Ridge Energy Storage
• Bob Haug, Iowa Municipal Utilities District
• Paul Denholm, University of Wisconsin, Madison
• Joseph DeCarolis, Carnegie Mellon University
• Al Cavallo, NIST